Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.     

A boundary-integral formulation for complex three-dimensional geometries

A new boundary-integral formulation is proposed to analyse the heat transfer in complex three-dimensional geometries. One example of such geometry is the die sets in the injection moulding process. Networks of cooling conduits within the mould and the closely spaced die surfaces require special attention both in formulation and numerical treatment of the integral equations. The proposed formulation couples the boundary formula, the gradient of the boundary formula and the exterior formula. The derivation of the integral equations is presented here along with an efficient method for integration of some of the kernels in these equations and a semi-analytical procedure for the integration of the highly singular integrands which result from differentiating the boundary formula. Although the techniques introduced here are applied to a particular problem in heat transfer, their potential application is much broader.     

Structural Behavior of Single Key Joints in Precast Concrete Segmental Bridges

A group of five full-depth male–female shear key specimens were match cast and tested to examine the shear capacity of epoxy-jointed single keys. Another group of four specimens were match cast using full-scale dimensions of a segmental construction bridge deck system for testing the fatigue and water tightness at a segment joint. Both cold-weather and hot-weather epoxy types were used to join the specimens. In addition to the experimental testing, finite-element analysis was also used to model the static response of the joint specimens. The observed failure mode of all shear-key specimens was fracture of concrete along the joint with shearing of the key. Good agreement was observed between the experimental test results and the finite-element analysis in terms of the failure mode of unreinforced specimen and the load of crack initiation of the specimens. Fatigue loading had a minor effect on the behavior of the posttensioning bars. The contribution of either the cold-weather or hot-weather epoxies to the joint shear strength was significant knowing that for similar concrete properties, the hot-weather epoxy specimens showed an increase of about 28% in the shear capacity, in comparison to the cold-weather epoxy specimens. The excellent performance of the epoxy-jointed shear keys was verified by field application on a prototype model simulating a portion of the Wacker Drive Bridge system. It was concluded that implementing AASHTO procedures result in conservative estimates of the shear strength of the single keyed joint since it neglects the contribution of the epoxy and underestimates the strength of the key itself.     

Emerging Advances of Nanotechnology in Drug and Vaccine Delivery against Viral Associated Respiratory Infectious Diseases (VARID)

Viral-associated respiratory infectious diseases are one of the most prominent subsets of respiratory failures, known as viral respiratory infections (VRI). VRIs are proceeded by an infection caused by viruses infecting the respiratory system. For the past 100 years, viral associated respiratory epidemics have been the most common cause of infectious disease worldwide. Due to several drawbacks of the current anti-viral treatments, such as drug resistance generation and non-targeting of viral proteins, the development of novel nanotherapeutic or nano-vaccine strategies can be considered essential. Due to their specific physical and biological properties, nanoparticles hold promising opportunities for both anti-viral treatments and vaccines against viral infections. Besides the specific physiological properties of the respiratory system, there is a significant demand for utilizing nano-designs in the production of vaccines or antiviral agents for airway-localized administration. SARS-CoV-2, as an immediate example of respiratory viruses, is an enveloped, positive-sense, single-stranded RNA virus belonging to the coronaviridae family. COVID-19 can lead to acute respiratory distress syndrome, similarly to other members of the coronaviridae. Hence, reviewing the current and past emerging nanotechnology-based medications on similar respiratory viral diseases can identify pathways towards generating novel SARS-CoV-2 nanotherapeutics and/or nano-vaccines.     

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.     

Synthesis and Evaluation of Semiconductive Polymer Compositions

A semiconductive polymer composition was prepared in which micronized particulate conductive filler, such as copper or graphite, is dispersed in a polymeric component that is of an essentially amorphous thermoplastic resin (such as polymethylmethacrylate). These molding powders of polymethylmethacrylate were prepared by suspension polymerization of methylmethacrylate monomer in presence of these conductors. The prepared molding powder contains 4.8% and 40.44% by volume (with respect to polymethylmethacrylate (PMMA)) of copper and graphite, respectively.

The effect of blending copper and graphite at different vol % to the prepared molding powder on the specific resistance of the product was studied.

It was concluded that the specific resistance of molding powder was decreased from 2.1 × 10¹¹ to 3.05 × 10⁶ ohm.cm at 70% volume copper and from 2.3 × 10⁴ to 2 × 10² ohm cm at 80% volume graphite.